Inferential statistics aims to draw conclusions from a sample of data. This typically involves specific hypotheses that you believe might explain the data. This training will introduce you to the framework of null hypothesis testing, a simple test of mean differences (t-test), p-values, what they really mean and how to adopt them in your research.
This workshop requires the following pre-knowledge:
- Familiarity with descriptive statistics, minimally: mean, standard deviation and variance.
- Minimal working knowledge of R/RStudio and ability to use the dplyr pipe (%>%), mutate(), filter(), and summarise() is beneficial but not required.
- Minimal working knowledge of using Jupyter notebooks with Python (Pandas, NumPy, Matplotlib) is useful but not required.
- Explain and critique the conceptual basis of null hypothesis significance testing.
- Interpret and evaluate p-values within the context of statistical inference.
- Conduct and report on a t-test in R or Python.
By attending this course, you will become familiar with the following skills:
- Critical evaluation skills for interpreting and assessing research that uses hypothesis testing.
- Programming skills in R for performing and interpreting hypothesis tests.
